
"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class 			lowercase					( lowercase__		):
			lowercase        =     42





class 			lowercase					( lowercase__   ,lowercase__		):






			@register_to_config
			def __init__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	int = 3      ,SCREAMING_SNAKE_CASE_  :	int = 3      ,SCREAMING_SNAKE_CASE_  :	Tuple[str] = ("DownEncoderBlock2D",)      ,SCREAMING_SNAKE_CASE_  :	Tuple[str] = ("UpDecoderBlock2D",)      ,SCREAMING_SNAKE_CASE_  :	Tuple[int] = (64,)      ,SCREAMING_SNAKE_CASE_  :	int = 1      ,SCREAMING_SNAKE_CASE_  :	str = "silu"      ,SCREAMING_SNAKE_CASE_  :	int = 3      ,SCREAMING_SNAKE_CASE_  :	int = 32      ,SCREAMING_SNAKE_CASE_  :	int = 256      ,SCREAMING_SNAKE_CASE_  :	int = 32      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	float = 0.1_82_15      ,SCREAMING_SNAKE_CASE_  :	str = "group"      ,)       ->	str:





				"""simple docstring"""


				super().__init__()

				# pass init params to Encoder
				lowerCAmelCase												=  Encoder(
				    in_channels=SCREAMING_SNAKE_CASE_      ,out_channels=SCREAMING_SNAKE_CASE_      ,down_block_types=SCREAMING_SNAKE_CASE_      ,block_out_channels=SCREAMING_SNAKE_CASE_      ,layers_per_block=SCREAMING_SNAKE_CASE_      ,act_fn=SCREAMING_SNAKE_CASE_      ,norm_num_groups=SCREAMING_SNAKE_CASE_      ,double_z=SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  vq_embed_dim if vq_embed_dim is not None else latent_channels

				lowerCAmelCase												=  nn.Convad(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,1			)
				lowerCAmelCase												=  VectorQuantizer(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,beta=0.25      ,remap=SCREAMING_SNAKE_CASE_      ,sane_index_shape=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  nn.Convad(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,1			)

				# pass init params to Decoder
				lowerCAmelCase												=  Decoder(
				    in_channels=SCREAMING_SNAKE_CASE_      ,out_channels=SCREAMING_SNAKE_CASE_      ,up_block_types=SCREAMING_SNAKE_CASE_      ,block_out_channels=SCREAMING_SNAKE_CASE_      ,layers_per_block=SCREAMING_SNAKE_CASE_      ,act_fn=SCREAMING_SNAKE_CASE_      ,norm_num_groups=SCREAMING_SNAKE_CASE_      ,norm_type=SCREAMING_SNAKE_CASE_      ,)






			@apply_forward_hook
			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	torch.FloatTensor      ,SCREAMING_SNAKE_CASE_  :	bool = True			)       ->	VQEncoderOutput:





				"""simple docstring"""


				lowerCAmelCase												=  self.encoder(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.quant_conv(SCREAMING_SNAKE_CASE_			)

				if not return_dict:
					return (h,)

				return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE_			)






			@apply_forward_hook
			def 					UpperCAmelCase							(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	torch.FloatTensor      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = True			)       ->	Union[DecoderOutput, torch.FloatTensor]:





				"""simple docstring"""


				if not force_not_quantize:
					lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  self.quantize(SCREAMING_SNAKE_CASE_			)
				else:
					lowerCAmelCase												=  h
				lowerCAmelCase												=  self.post_quant_conv(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.decoder(SCREAMING_SNAKE_CASE_      ,quant if self.config.norm_type == '''spatial''' else None			)

				if not return_dict:
					return (dec,)

				return DecoderOutput(sample=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	torch.FloatTensor      ,SCREAMING_SNAKE_CASE_  :	bool = True			)       ->	Union[DecoderOutput, torch.FloatTensor]:





				"""simple docstring"""


				lowerCAmelCase												=  sample
				lowerCAmelCase												=  self.encode(SCREAMING_SNAKE_CASE_			).latents
				lowerCAmelCase												=  self.decode(SCREAMING_SNAKE_CASE_			).sample

				if not return_dict:
					return (dec,)

				return DecoderOutput(sample=SCREAMING_SNAKE_CASE_			)




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


